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Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

As the number of Internet purchases of fashion items increases, the problem of inaccurate color representation on the Web becomes more significant. Color inaccuracy has many negative consequences for marketers, including loss of sales, increased returns and complaints, and customer defections. This research reports the findings of a survey conducted as part of an initial investigation into consumer opinions about fashion merchandise purchasing over the Internet. Results indicate that companies are losing customers and sales as a result of having colors on e‐commerce sites that do not accurately represent the actual colors of the products being sold. Increased dissatisfaction on the part of consumers leads to greater costs in both customer service and reverse logistics. Further, a majority of the respondents indicated that they would not make additional purchases from an e‐tailer if they received items in colors different than they expected. The paper concludes with suggestions for future research.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

This paper aims to determine the characteristics of board certification among US physicians and to test whether accounting for the expected gains to certification alters the pattern of the determinants of board certification.

Splitting the sample into sub‐samples by characteristics associated with certification/non‐certification identified in a probit, the incremental gain to certification from log‐earnings equations is identified. Realizing that these methods are susceptible to sample selection, correction is made for it using the Heckman approach. Using the sample selection corrected equations, the expected gain to certification among those who certify is then predicted and those who do not certify is then predicted and this difference is included as a proxy for the expected gain in the original probit to ascertain whether including the expected gain alters the determinants of certification.

Accounting for the expected gain alters the pattern of the determinants of certification. Although some groups such as blacks appear less likely to certify, after accounting for their expected return to certification, they are not as less likely. This is explained in terms of the expected marginal return to certification, market structure and practice setting.

The data used in the analysis apply only to young physicians in the USA. Also, these results may be applicable only to the particular cohort used in this analysis.

The findings help to explain the absence of minority board certified physicians within the USA.

This paper is the first to simultaneously estimate the returns to physician board certification and the decision to obtain certification.